Advertisement

Temporary User States Method to Support Home Habitants

  • Ewelina SzczekockaEmail author
Conference paper
  • 14 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11878)

Abstract

Major goal of the research introduced in the paper is elaboration of a method delivering temporary user states to personalize data-based services for this user. The method addressing a paradigm of ambient living will be used for support people broadly in their everyday life activities, like habitants in their home environments, persons needing assistance (in Ambient Assist Living) and others. It can be applied in new data-based services on 5G platforms, using intelligent ambient environments. A system for home experimentally developed in Orange Labs is one of the solutions where the method can be implemented. This system is aligned with an idea of sensitive home, discovering habitant’s affective characteristics, like personality and emotions, based on his data and reacting according to this characteristics. The paper aims at presentation of another affective characteristics based on discovering temporary user states. An important aspect considered is privacy, GDPR compliance and moreover it should include a consideration on ethics.

Keywords

Home habitants support Sensitive home Ambient assisted living NLP Big data application 

References

  1. 1.
    White Paper: Digital Transformation Initiative. Telecommunication Industry, WEF in Coop. with Accenture (2017). https://www.weforum.org/whitepapers/digital-transformation-initiative
  2. 2.
    Eurpoean Commission Report: The Rise of Virtual Personal Assistants (2018). https://ec.europa.eu/growth/tools-databases/dem/monitor/content/rise-virtual-personal-assistants
  3. 3.
    Hildebrandt, M.: Defining profiling: a new type of knowledge? In: Hildebrandt, M., Gutwirth, S. (eds.) Profiling the European Citizen. Springer, Dordrecht (2008).  https://doi.org/10.1007/978-1-4020-6914-7_2CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Kanoje, S., Girase, S., Mukhopadhyay, D.: User profiling trends, techniques and applications. Int. J. Adv. Found. Res. Comput. 1, 2348–4853 (2014)Google Scholar
  6. 6.
    Bosco, F., Creemers, N., Ferraris, V., Guagnin, D., Koops, B.-J.: Profiling technologies and fundamental rights and values: regulatory challenges and perspectives from European data protection authorities. In: Gutwirth, S., Leenes, R., de Hert, P. (eds.) Reforming European Data Protection Law. LGTS, vol. 20, pp. 3–33. Springer, Dordrecht (2015).  https://doi.org/10.1007/978-94-017-9385-8_1CrossRefGoogle Scholar
  7. 7.
    Jaquet-Chiffelle, D.O., Benoist, E., Haenni, R., Wenger, F., Zwingelberg, H.: Virtual persons and identities. In: Rannenberg, K., Royer, D., Deuker, A. (eds.) The Future of Identity in the Information Society. Springer, Berlin (2009).  https://doi.org/10.1007/978-3-642-01820-6_3CrossRefGoogle Scholar
  8. 8.
    Ren, L., Xie, K., Chen, L., Yu, K.: Towards universal dialogue state tracking. In: EMNLP 2018 (2018).  https://doi.org/10.18653/v1/d18-1299
  9. 9.
    Callejas, Z., Griol, D., López-Cózar, R.: EURASIP J. Adv. Signal Process. 2011, 6 (2011).  https://doi.org/10.1186/1687-6180-2011-6CrossRefGoogle Scholar
  10. 10.
    de Montjoye, Y.-A., Quoidbach, J., Robic, F., Pentland, A.S.: Predicting personality using novel mobile phone-based metrics. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 48–55. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37210-0_6CrossRefGoogle Scholar
  11. 11.
    Kern, M., et al.: The online social self an open vocabulary approach to personality. Assessment 21, 158–169 (2013).  https://doi.org/10.1177/1073191113514104CrossRefGoogle Scholar
  12. 12.
    Xu, R., Frey, R.M., Fleisch, E., Ilic, A.: Understanding the impact of personality traits on mobile app adoption - insights from a large-scale field study. Comput. Hum. Behav. 62, 244–256 (2016).  https://doi.org/10.1016/j.chb.2016.04.011CrossRefGoogle Scholar
  13. 13.
    Liu, L., Preotiuc-Pietro, D., et al.: Analyzing personality through social media profile picture choice. In: ICWSM 2016, Association for the Advancement of Artificial Intelligence (2016)Google Scholar
  14. 14.
    Kałużny, P.: Behavioural profiling authentication based on trajectory based anomaly detection model of user’s mobility. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 303, pp. 242–254. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69023-0_21CrossRefGoogle Scholar
  15. 15.
    Blondel, V., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008).  https://doi.org/10.1088/1742-5468/2008/10/P10008CrossRefzbMATHGoogle Scholar
  16. 16.
    Khayrullin, R.M., Makarov, I., Zhukov, L.E.: Predicting psychology attributes of a social network user. In: MPRA Paper 82810. University Library of Munich, Germany (2017). https://ideas.repec.org/p/pra/mprapa/82810.html
  17. 17.
    Kabzińska, K., Wieloch, M., Filipiak, D., Filipowska, A.: Profiling user’s personality using colours: connecting BFI-44 personality traits and plutchik’s wheel of emotions. In: Wilimowska, Z., Borzemski, L., Świątek, J. (eds.) ISAT 2018. AISC, vol. 854, pp. 371–380. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-99993-7_33CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Informatics and Electronic EconomyPoznan University of EconomicsPoznańPoland
  2. 2.R&D LabsOrange PolskaWarsawPoland

Personalised recommendations